Methodological Issues in Predicting Pediatric Epilepsy Surgery Candidates Through Natural Language Processing and Machine Learning.
Methodological Issues in Predicting Pediatric Epilepsy Surgery Candidates Through Natural Language Processing and Machine Learning.
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DOI：
10.4137/bii.s38308
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Pestian J
中科院分区：
文献类型：
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作者：
Cohen KB;Glass B;Greiner HM;Holland-Bouley K;Standridge S;Arya R;Faist R;Morita D;Mangano F;Connolly B;Glauser T;Pestian J
目的：我们描述了一个系统的开发和评估，该系统使用机器学习和自然语言处理技术来识别耐药性儿科癫痫手术干预的潜在候选人。这些数据由从电子健康记录（EHR）中提取的自由文本临床记录组成。来自EHR和手动图表注释的已知临床结果都为患者状态提供了黄金标准。然后测试以下假设：1）机器学习方法可以像医生一样识别癫痫手术候选人，2）机器学习方法可以比医生更早地识别候选人。通过系统地评估数据源、训练数据量、类平衡、分类算法和特征集对分类器性能的影响来测试这些假设。结果支持这两个假设，F-措施范围从0.71到0.82。特征集、分类算法、训练数据量、类平衡和金标准都显著影响分类性能。进一步观察到，即使在记录手术转诊前一年，分类性能也优于两个注释者之间的最高一致性。结果表明，这种机器学习方法有助于预测儿科癫痫手术候选人，并减少手术转诊的滞后时间。
Objective: We describe the development and evaluation of a system that uses machine learning and natural language processing techniques to identify potential candidates for surgical intervention for drug-resistant pediatric epilepsy. The data are comprised of free-text clinical notes extracted from the electronic health record (EHR). Both known clinical outcomes from the EHR and manual chart annotations provide gold standards for the patient’s status. The following hypotheses are then tested: 1) machine learning methods can identify epilepsy surgery candidates as well as physicians do and 2) machine learning methods can identify candidates earlier than physicians do. These hypotheses are tested by systematically evaluating the effects of the data source, amount of training data, class balance, classification algorithm, and feature set on classifier performance. The results support both hypotheses, with F-measures ranging from 0.71 to 0.82. The feature set, classification algorithm, amount of training data, class balance, and gold standard all significantly affected classification performance. It was further observed that classification performance was better than the highest agreement between two annotators, even at one year before documented surgery referral. The results demonstrate that such machine learning methods can contribute to predicting pediatric epilepsy surgery candidates and reducing lag time to surgery referral.